During the last year, we made 4 major advances in neuronal avalanche research: 1. We demonstrated that from the scale-invariant, and threshold-independent neuronal avalanche dynamics arise new, threshold-dependent coherence dynamics that might allow for basic decision-making processes at the population level (Thiagarajan et. al., 2010). Summary: Perception and behavior are thought to arise from transient associations among sub-groups of nerve cells in the brain. However, identifying which of the many active neurons are associated at any given time and how poses a challenge. Here we show that when the composite activity of a local group of cortical neurons, measured as a complex waveform in the extracellular field, exceeds a threshold, its activity pattern extending up to hundreds of milliseconds occurs without distortion at other cortical sites via fast synaptic transmission. We call these all-or-none propagated patterns coherence potentials, in analogy to action potentials at the single cell level. In contrast to action potentials, which are stereotypical and thus capable only of binary coding, coherence potentials are diverse and complex waveforms that can serve as a high-dimensional parameter for encoding information. The non-linear relationship between local activity and its extent of replicated spread suggests a tipping point that bears analogy to the propagation of innovations and economic behavior in social networks, which can spread rapidly once they have garnered a local critical mass. 2. We demonstrated that cortical networks with avalanche dynamics are most sensitive to a wide range of inputs (Shew et al., 2009). Summary: Spontaneous neuronal activity is a ubiquitous feature of cortex. Its spatiotemporal organization reflects past input and modulates future network output. Here we study whether a particular type of spontaneous activity is generated by a network that is optimized for input processing. Neuronal avalanches are a type of spontaneous activity observed in superficial cortical layers in vitro and in vivo with statistical properties expected from a network operating at "criticality." Theory predicts that criticality and, therefore, neuronal avalanches are optimal for input processing, but until now, this has not been tested in experiments. Here, we use cortex slice cultures grown on planar microelectrode arrays to demonstrate that cortical networks that generate neuronal avalanches benefit from a maximized dynamic range, i.e., the ability to respond to the greatest range of stimuli. By changing the ratio of excitation and inhibition in the cultures, we derive a network tuning curve for stimulus processing as a function of distance from criticality in agreement with predictions from our simulations. Our findings suggest that in the cortex, (1) balanced excitation and inhibition establishes criticality, which maximizes the range of inputs that can be processed, and (2) spontaneous activity and input processing are unified in the context of critical phenomena. 3. With our collaborators at RIKEN, we showed that neuronal avalanche dynamics are best described by a hierarchical dynamical model, in contrast to the widely held view that pairwise (flat) models are sufficient (Santos et al., 2010). Summary: Recent advances in the analysis of neuronal activities suggest that the instantaneous activity patterns can be mostly explained by considering only first-order and pairwise interactions between recorded elements, i.e., action potentials or local field potentials (LFP), and do not require higher-than-pairwise-order interactions. If generally applicable, this pairwise approach greatly simplifies the description of network interactions. However, an important question remains: are the recorded elements the units of interaction that best describe neuronal activity patterns? To explore this, we recorded spontaneous LFP peak activities in cortical organotypic cultures using planar, integratted 60-microelectrode arrays. We compared predictions obtained using a pairwise approach with those using a hierarchical approach that uses two different spatial units for describing the activity interactions: single electrodes and electrode clusters. In this hierarchical model, short-range interactions within each cluster were modeled by pairwise interactions of electrode activities and long-range interactions were modeled by pairwise interactions of cluster activities. Despite the relatively low number of parameters used, the hierarchical model provided a more accurate description of the activity patterns than the pairwise model when applied to ensembles of 10 electrodes. Furthermore, the hierarchical model was successfully applied to a larger-scale data of 60 electrodes. Electrode activities within clusters were highly correlated and spatially contiguous. In contrast, long-range interactions were diffuse, suggesting the presence of higher-than-pairwise-order interactions involved in the LFP peak activities. Thus, the identification of appropriate units of interaction may allow for the successful characterization of neuronal activities in large-scale networks. 4. In collaboration with the MPI Frankfurt, we demonstrated for the first time spike avalanches in a purely sensory area, the visual cortex of the anesthetized cat (Hahn et al., 2010). Summary: Many complex systems give rise to events that are clustered in space and time thereby establishing a correlation structure that is governed by power law statistics. In the cortex, such clusters of activity, called 'neuronal avalanches', were recently found in local field potentials (LFP) of spontaneous activity in acute cortex slices, slice cultures, in the developing cortex of the anesthetized rat, and in premotor and motor cortex of awake monkeys. At present it is unclear whether neuronal avalanches also exist in the spontaneous LFP and spike activity in vivo in sensory areas of the mature brain. To address this question, we recorded spontaneous LFP and extracellular spiking activity with multiple 4x4 microelectrode arrays (Michigan Probes) in area 17 of adult cats under anesthesia. A cluster of events was defined as a consecutive sequence of time bins Dt (1 - 32 ms), each containing at least one LFP event or spike anywhere on the array. LFP cluster sizes consistently distributed according to a power law with a slope largely above -1.5. In two thirds of the corresponding experiments, spike clusters also displayed a power law that displayed a slightly steeper slope of -1.8 and was destroyed by sub-sampling operations. The power law in spike clusters was accompanied with stronger temporal correlations between spiking activities of neurons that spanned also longer time periods as compared to spike clusters lacking power law statistics. The results suggest that spontaneous activity of the visual cortex under anesthesia has the properties of neuronal avalanches. 5. TECHNIQUES/METHODS Progress: In our effort to improve in vitro conditions for systems dynamic research, we successfully tested (collab. Ron McKay/NINDS) angiogenic factors that maintain vascular structure and increase the density of dopamine neuron processes (Androutsellis-Theotokis et al., 2010). In order to take advantage of 2-photon imaging for neuronal avalanche research, we developed technology that allows us to simultaneously excite a tissue with laser light to collect fluorescence and record neuronal avalanches with chip-based microelectrode arrays (Shew et al., 2010).